Working Papers:

Trust and Information Demand with Biased Preferences: A Prediction Game Experiment

Information demand and utilization is an increasingly relevant and influential process in the digital era. People have access to limitless sources and content, making the methodology of how people analyze information and form beliefs an increasingly important area of research. My experiment improves our understanding of biased payoffs affect information demand and utilization in an environment where participants report predictions over a series of rounds about a binary `state'. They are paid for each correct prediction. Participants bid to observe either a private signal or a prediction made by an expert in order to form an informative posterior belief about the state. In certain sessions I provide an additional, `asymmetric' financial incentive to either the participants or experts each round they predict a certain state. Doing so introduces a biased preference between outcomes. I find that participants are generally unaffected by the asymmetric payoff schemes when demanding for information. However, their ex post utilization of received information is significantly altered. Participants are much less likely to utilize information when it indicates a state with an inferior payoff. Similarly, participants are less likely to utilize predictions made by experts which are consistent with the experts' bias, albeit the treatment effect is smaller. These results persists even when the information sources are extremely precise. My results formally exhibit a disconnect between information demand and information utilization in biased settings. Systems which rely on reported demand (such as social media interactions) to extrapolate the value of information are therefore not consistent with the revealed ex post preferences of people as they attempt to make decisions with that information. (LINK)

Free Riding Toward Personal Protection: Relating Parental Cooperative Behavior With Vaccine Hesitancy (with Aaron Enriquez and Mariah Ehmke)

Small urban clusters and rural communities, which have historically had low vaccination rates, are especially vulnerable to healthcare system overloads. We conducted a study in which parents from such locations played a Voluntary Contribution Mechanism experiment and then answered survey questions about influenza vaccinations. We observed parents’ cooperative actions in the experiment (i.e., contributions to a shared group account) and their relationship with flu vaccination decisions for themselves and their child. This article classifies different player-types based on parents’ propensities to cooperate and react to their partners’ actions, including “free riders” (keep the majority of tokens), “contributors” (contribute the majority of tokens), and “conditional cooperators” (adjust contributions based on their partner’s actions). We also control for the intensity of reciprocation among all players. We find that free riders and parents who tend to reciprocate are the most likely to vaccinate. Our result about free riders is a departure from previous literature. The findings shed light on behavioral motives behind people’s vaccination decisions. Policies that amplify free riding and reciprocation may increase vaccination rates, which would be critical for mitigating the damaging effects of COVID-19 and other preventable diseases. (LINK)

A Gamble of Life and Death (with Gregory Marchal and Mariah Ehmke)

Vaccine hesitancy in rural communities relates to outbreaks of vaccine preventable diseases like influenza, burdening healthcare systems with hospitalizations and deaths. Research on largely urban populations has shown that parents who mistrust the healthcare system perceive uncertainty on information related to vaccine efficacy and risks (i.e. side effects), leading to a decline in vaccine uptake. Our research objective is to test the role of parents’ economic risk preferences and vaccine information ambiguity in their decision to forego influenza vaccinations for their children and themselves. We collected data using a lab-in-field economic experiment to measure parents’ constant relative risk aversion coefficient (CRRA) and a survey to obtain data on their vaccine beliefs, practices, and information sources. The data were then analyzed using a logit model regression to test the role of economic risk preferences and vaccine information ambiguity in their influenza vaccination decisions. We control for trust in the healthcare system, community characteristics, and personal demographic information in the model estimation. We find parents’ influenza vaccination decisions are significantly dependent on their ambiguity aversion, but not their risk aversion CRRA measurements. Parents who perceive greater uncertainty in the risks of vaccines relative to the risks of diseases tend to vaccinate their children for the flu at lower rates. This relationship exists after controlling for trust in the healthcare system, suggesting that policies addressing the perceived ambiguity in the vaccination decision independent of healthcare trust may be most effective to reduce hesitancy. (LINK)

Selected Works-in-Progress:

Trust and Prior Incentives: The Demand for Information and Expertise Valuation

In a rapidly expanding digital world, information about decisions ranging from political support to restaurant choices is more available than ever before. The process of evaluating the trustworthiness of information sources can be arduous and arbitrary, yet it has important implications. Understanding the demand function for expertise about these decisions is critical as policymakers navigate a world where anyone can be an author and it is more difficult than ever to signal information quality. My experiment uses the Becker-DeGroot-Marschak (BDM) Method to elicit subjects' demand for signals from exogenously defind 'experts' about a binary state in a sequence of rounds. I then analyze the effect that asymmetric payoff schemes for either the decision maker or the expert have on the demand for information relative to a perfectly revealed information source. These results contribute to the growing literature that help policymakers convey expertise and provide a foundation for researching other behavioral confounders that may affect information demand.

Trust and Welfare Effects of Revealed Reputations in Information Cascades

As the connectedness of society increases and social networks grow larger, the number of personal decisions that use both private information and signals inferred from public actions is increasing. Furthermore, the growing number of people in networks means the combined influence of public actions is at an all-time high and increases the probability an information cascade starts. An information cascade occurs when the strength of others’ actions is greater than the informativeness of private information, after which it is optimal for all remaining undecided individuals to ignore their private signals and follow the public. While cascade-esque decision making is observed frequently in the field and helps explain various phenomena, it is uncommon for experimenters to find stable cascading behavior in the lab. Existing explanations for this beyond lapses in conditional updating ability are limited. My research contributes to the information cascade literature by studying the long-term welfare effects from providing more reliable information at the start of an information cascade through exogeneous sorting mechanisms indicating historical performance and ability. I find that - while cascade lengths do not change even when participants receive noisy signals that the quality of early information has improved - the enhanced quality of signals leads to significant welfare enhancements for low-ability individuals. (oTree Application and Code)